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Cost-sensitive boosting algorithms: Do we really need them?
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DOI：10.1007/s10994-016-5572-x.png)
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摘要
En 中文
We provide a unifying perspective for two decades of work on cost-sensitive Boosting algorithms. When analyzing the literature 1997-2016, we find 15 distinct cost-sensitive variants of the original algorithm; each of these has its own motivation and claims to superiority-so who should we believe? In this work we critique the Boosting literature using four theoretical frameworks: Bayesian decision theory, the functional gradient descent view, margin theory, and probabilistic modelling. Our finding is that only three algorithms are fully supported-and the probabilistic model view suggests that all require their outputs to be calibrated for best performance. Experiments on 18 datasets across 21 degrees of imbalance support the hypothesis-showing that once calibrated, they perform equivalently, and outperform all others. Our final recommendation-based on simplicity, flexibility and performance-is to use the original Adaboost algorithm with a shifted decision threshold and calibrated probability estimates.
Keyword:
Boosting
Cost-sensitive
Class imbalance
Classifier calibration
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2.9
论文数:
2.7K
被引数:
3.4W
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